| Approximation and inference methods for stochastic biochemical kinetics—a tutorial review D Schnoerr, G Sanguinetti, R Grima Journal of Physics A: Mathematical and Theoretical 50 (9), 093001, 2017 | 391 | 2017 |
| Comparison of different moment-closure approximations for stochastic chemical kinetics D Schnoerr, G Sanguinetti, R Grima The Journal of Chemical Physics 143 (18), 2015 | 130 | 2015 |
| A comprehensive network atlas reveals that Turing patterns are common but not robust NS Scholes, D Schnoerr, M Isalan, MPH Stumpf Cell systems 9 (3), 243-257. e4, 2019 | 98 | 2019 |
| The complex chemical Langevin equation D Schnoerr, G Sanguinetti, R Grima The Journal of chemical physics 141 (2), 2014 | 85 | 2014 |
| Validity conditions for moment closure approximations in stochastic chemical kinetics D Schnoerr, G Sanguinetti, R Grima The Journal of chemical physics 141 (8), 2014 | 74 | 2014 |
| Turing pattern design principles and their robustness ST Vittadello, T Leyshon, D Schnoerr, MPH Stumpf Philosophical Transactions of the Royal Society A 379 (2213), 20200272, 2021 | 49 | 2021 |
| Exactly solvable models of stochastic gene expression L Ham, D Schnoerr, RD Brackston, MPH Stumpf The Journal of Chemical Physics 152 (14), 2020 | 47 | 2020 |
| Cox process representation and inference for stochastic reaction–diffusion processes D Schnoerr, R Grima, G Sanguinetti Nature communications 7 (1), 11729, 2016 | 40 | 2016 |
| Efficient low-order approximation of first-passage time distributions D Schnoerr, B Cseke, R Grima, G Sanguinetti Physical review letters 119 (21), 210601, 2017 | 21 | 2017 |
| Expectation propagation for continuous time stochastic processes B Cseke, D Schnoerr, M Opper, G Sanguinetti Journal of Physics A: Mathematical and Theoretical 49 (49), 494002, 2016 | 21* | 2016 |
| Error estimates and specification parameters for functional renormalization D Schnoerr, I Boettcher, JM Pawlowski, C Wetterich Annals of Physics 334, 83-99, 2013 | 17 | 2013 |
| Time-dependent product-form Poisson distributions for reaction networks with higher order complexes DF Anderson, D Schnoerr, C Yuan Journal of Mathematical Biology 80, 1919-1951, 2020 | 15 | 2020 |
| Learning system parameters from turing patterns D Schnörr, C Schnörr Machine Learning 112 (9), 3151-3190, 2023 | 14 | 2023 |
| Neural field models for latent state inference: Application to large-scale neuronal recordings ME Rule, D Schnoerr, MH Hennig, G Sanguinetti PLoS computational biology 15 (11), e1007442, 2019 | 13 | 2019 |
| The design principles of discrete turing patterning systems T Leyshon, E Tonello, D Schnoerr, H Siebert, MPH Stumpf Journal of Theoretical Biology 531, 110901, 2021 | 12 | 2021 |
| Probabilistic model checking for continuous-time Markov chains via sequential Bayesian inference D Milios, G Sanguinetti, D Schnoerr Quantitative Evaluation of Systems: 15th International Conference, QEST 2018 …, 2018 | 8 | 2018 |
| An alternative route to the system-size expansion C Cianci, D Schnoerr, A Piehler, R Grima Journal of Physics A: Mathematical and Theoretical 50 (39), 395003, 2017 | 6 | 2017 |
| Probabilistic model checking for continuous time markov chains via sequential bayesian inference D Milios, G Sanguinetti, D Schnoerr arXiv preprint arXiv:1711.01863, 2017 | 5 | 2017 |
| Turing patterns are common but not robust NS Scholes, D Schnoerr, M Isalan, M Stumpf bioRxiv, 352302, 2018 | 2 | 2018 |
| Approximation methods and inference for stochastic biochemical kinetics DB Schnoerr The University of Edinburgh, 2016 | 1 | 2016 |